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Construction and Prediction of a Dynamic Multi-relationship Bipartite Network

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Neural Information Processing (ICONIP 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1965))

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Abstract

Bipartite networks are capable of representing complex systems that involve two distinct types of objects. However, there are limitations to the existing bipartite networks: 1) It is inadequate in characterizing multi-relationships among objects in complex systems, as it is restricted to depict only one type of relationship. 2) It is limited to static representations of complex systems, hampering their ability to describe dynamic changes in the interactions among objects over time. Therefore, the Dynamic Multi-Relationship Bipartite Network (DMBN) model is introduced, which not only models the dynamic multi-relationships between two types of objects in complex systems, but also enables dynamic prediction of the intricate relationships between objects. Extensive experiments were conducted on complex systems, and the results indicate that the DMBN model is significantly better than the baseline methods across multiple evaluation metrics, thereby proving the effectiveness of the DMBN.

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Authors and Affiliations

  1. School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China

    Hehe Lv & Guobing Zou

  2. School of Computer and Information Engineering, Shanghai Polytechnic University, Shanghai, 201209, China

    Bofeng Zhang

Authors
  1. Hehe Lv
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  2. Guobing Zou
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  3. Bofeng Zhang
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Corresponding author

Correspondence to Guobing Zou .

Editor information

Editors and Affiliations

  1. School of Automation, Central South University, Changsha, China

    Biao Luo

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Long Cheng

  3. Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, China

    Zheng-Guang Wu

  4. School of Automation, Guangdong University of Technology, Guangzhou, China

    Hongyi Li

  5. School of Electrical Engineering and Telecommunications, UNSW Sydney, Sydney, NSW, Australia

    Chaojie Li

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Lv, H., Zou, G., Zhang, B. (2024). Construction and Prediction of a Dynamic Multi-relationship Bipartite Network. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1965. Springer, Singapore. https://doi.org/10.1007/978-981-99-8145-8_25

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  • DOI: https://doi.org/10.1007/978-981-99-8145-8_25

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-8144-1

  • Online ISBN: 978-981-99-8145-8

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